SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 8251–8300 of 10307 papers

TitleStatusHype
TransNet: Transferable Neural Networks for Partial Differential Equations—0
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback—0
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning—0
Triangular Transfer: Freezing the Pivot for Triangular Machine Translation—0
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning—0
Trivial Transfer Learning for Low-Resource Neural Machine Translation—0
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation—0
Trustworthy Transfer Learning: A Survey—0
TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles—0
TuneNSearch: a hybrid transfer learning and local search approach for solving vehicle routing problems—0
Tuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation—0
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis—0
TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data—0
Two Front-Ends, One Model : Fusing Heterogeneous Speech Features for Low Resource ASR with Multilingual Pre-Training—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews—0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis—0
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles—0
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization—0
UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTa—0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches—0
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere—0
Ear-Keeper: Real-time Diagnosis of Ear Lesions Utilizing Ultralight-Ultrafast ConvNet and Large-scale Ear Endoscopic Dataset—0
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention—0
Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task—0
Unbiased Scene Graph Generation using Predicate Similarities—0
Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation—0
Uncertainty-aware Incremental Learning for Multi-organ Segmentation—0
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation—0
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading—0
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning—0
Uncertainty in Multitask Transfer Learning—0
Uncertainty Regularized Multi-Task Learning—0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction—0
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes—0
Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray—0
Understanding and Improving Information Transfer in Multi-Task Learning—0
Understanding and Leveraging the Learning Phases of Neural Networks—0
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Understanding Cross-Lingual Inconsistency in Large Language Models—0
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models—0
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis—0
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning—0
Understanding Social Networks using Transfer Learning—0
Understanding the Benefits of Image Augmentations—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified